code: def __init__(
      self, logger, metric="l1", random_state=None, metric_params=None
  ):
      self.logger = logger
  
      self.metric = metric
      self.model = None
      self.metric_params = metric_params
      self.metafeatures = None
      self.runs = None
      self.best_configuration_per_dataset = None
      self.random_state = sklearn.utils.check_random_state(random_state)
      self.scaler = MinMaxScaler()
  
      if self.metric_params is None:
          self.metric_params = {}
  
file_path: auto-sklearn/autosklearn/metalearning/metalearning/kNearestDatasets/kND.py
imports: import numpy
  import pandas
  import sklearn.utils
  from sklearn.neighbors import NearestNeighbors
  from sklearn.preprocessing import MinMaxScaler
label_desc: Use image node_id 1 to create a new KNearestDatasets object from inherited base classes: object with example: obj = KNearestDatasets(logger, metric, random_state, metric_params)